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docs/docs/concepts/persistence.md

@@ -471,7 +471,7 @@ Second, checkpointers allow for ["memory"](agentic_concepts.md#memory) between i
 
 ### Time Travel
 
-Third, checkpointers allow for ["time travel"](../how-tos/human_in_the_loop/time-travel.ipynb), allowing users to replay prior graph executions to review and / or debug specific graph steps. In addition, checkpointers make it possible to fork the graph state at arbitrary checkpoints to explore alternative trajectories.
+Third, checkpointers allow for ["time travel"](time-travel.md), allowing users to replay prior graph executions to review and / or debug specific graph steps. In addition, checkpointers make it possible to fork the graph state at arbitrary checkpoints to explore alternative trajectories.
 
 ### Fault-tolerance
 

docs/docs/concepts/time-travel.md

@@ -0,0 +1,72 @@
+# Time Travel ⏱️
+
+!!! note "Prerequisites"
+
+    This guide assumes that you are familiar with LangGraph's checkpoints and states. If not, please review the [persistence](./persistence.md) concept first.
+
+
+When working with non-deterministic systems that make model-based decisions (e.g., agents powered by LLMs), it can be useful to examine their decision-making process in detail:
+
+1. 🤔 **Understand Reasoning**: Analyze the steps that led to a successful result.
+2. 🐞 **Debug Mistakes**: Identify where and why errors occurred.
+3. 🔍 **Explore Alternatives**: Test different paths to uncover better solutions.
+
+We call these debugging techniques **Time Travel**, composed of two key actions: [**Replaying**](#replaying) 🔁 and [**Forking**](#forking) 🔀 .
+
+## Replaying
+
+![](./img/human_in_the_loop/replay.png)
+
+Replaying allows us to revisit and reproduce an agent's past actions. This can be done either from the current state (or checkpoint) of the graph or from a specific checkpoint.
+
+To replay from the current state, simply pass `None` as the input along with a `thread`:
+
+```python
+thread = {"configurable": {"thread_id": "1"}}
+for event in graph.stream(None, thread, stream_mode="values"):
+    print(event)
+```
+
+To replay actions from a specific checkpoint, start by retrieving all checkpoints for the thread:
+
+```python
+all_checkpoints = []
+for state in graph.get_state_history(thread):
+    all_checkpoints.append(state)
+```
+
+Each checkpoint has a unique ID. After identifying the desired checkpoint, for instance, `xyz`, include its ID in the configuration:
+
+```python
+config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xyz'}}
+for event in graph.stream(None, config, stream_mode="values"):
+    print(event)
+```
+
+The graph efficiently replays previously executed nodes instead of re-executing them, leveraging its awareness of prior checkpoint executions.
+
+## Forking
+
+![](./img/human_in_the_loop/forking.png)
+
+Forking allows you to revisit an agent's past actions and explore alternative paths within the graph.
+
+To edit a specific checkpoint, such as `xyz`, provide its `checkpoint_id` when updating the graph's state:
+
+```python
+config = {"configurable": {"thread_id": "1", "checkpoint_id": "xyz"}}
+graph.update_state(config, {"state": "updated state"}, )
+```
+
+This creates a new forked checkpoint, xyz-fork, from which you can continue running the graph:
+
+```python
+config = {'configurable': {'thread_id': '1', 'checkpoint_id': 'xyz-fork'}}
+for event in graph.stream(None, config, stream_mode="values"):
+    print(event)
+```
+
+## Additional Resources 📚
+
+- [**Conceptual Guide: Persistence**](https://langchain-ai.github.io/langgraph/concepts/persistence/#replay): Read the persistence guide for more context on replaying.
+- [**How to View and Update Past Graph State**](../how-tos/human_in_the_loop/time-travel.ipynb): Step-by-step instructions for working with graph state that demonstrate the **replay** and **fork** actions.